{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71332d6c-3936-4c26-9171-5542767e96b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['北京', '天津', '河北', '山西', '内蒙古', '辽宁', '吉林', '黑龙江', '上海', '江苏', '浙江',\n",
       "       '安徽', '福建', '江西', '山东', '河南', '湖南', '湖北', '广东', '广西', '海南', '重庆',\n",
       "       '四川', '贵州', '云南', '西藏', '陕西', '甘肃', '青海', '宁夏', '新疆'], dtype=object)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "city=pd.read_csv(\"D:/Users/19202/Desktop/city.txt\",encoding='gbk',header=None,engine='python')\n",
    "cityName=city.iloc[:,0].values\n",
    "cityName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f098ab9e-929d-48fc-b53c-3dfb6e0270e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2959.19</td>\n",
       "      <td>730.79</td>\n",
       "      <td>749.41</td>\n",
       "      <td>513.34</td>\n",
       "      <td>467.87</td>\n",
       "      <td>1141.82</td>\n",
       "      <td>478.42</td>\n",
       "      <td>457.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2459.77</td>\n",
       "      <td>495.47</td>\n",
       "      <td>697.33</td>\n",
       "      <td>302.87</td>\n",
       "      <td>284.19</td>\n",
       "      <td>735.97</td>\n",
       "      <td>570.84</td>\n",
       "      <td>305.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1495.63</td>\n",
       "      <td>515.90</td>\n",
       "      <td>362.37</td>\n",
       "      <td>285.32</td>\n",
       "      <td>272.95</td>\n",
       "      <td>540.58</td>\n",
       "      <td>364.91</td>\n",
       "      <td>188.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1406.33</td>\n",
       "      <td>477.77</td>\n",
       "      <td>290.15</td>\n",
       "      <td>208.57</td>\n",
       "      <td>201.50</td>\n",
       "      <td>414.72</td>\n",
       "      <td>281.84</td>\n",
       "      <td>212.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1303.97</td>\n",
       "      <td>524.29</td>\n",
       "      <td>254.83</td>\n",
       "      <td>192.17</td>\n",
       "      <td>249.81</td>\n",
       "      <td>463.09</td>\n",
       "      <td>287.87</td>\n",
       "      <td>192.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1730.84</td>\n",
       "      <td>553.90</td>\n",
       "      <td>246.91</td>\n",
       "      <td>279.81</td>\n",
       "      <td>239.18</td>\n",
       "      <td>445.20</td>\n",
       "      <td>330.24</td>\n",
       "      <td>163.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1561.86</td>\n",
       "      <td>492.42</td>\n",
       "      <td>200.49</td>\n",
       "      <td>218.36</td>\n",
       "      <td>220.69</td>\n",
       "      <td>459.62</td>\n",
       "      <td>360.48</td>\n",
       "      <td>147.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1410.11</td>\n",
       "      <td>510.71</td>\n",
       "      <td>211.88</td>\n",
       "      <td>277.11</td>\n",
       "      <td>224.65</td>\n",
       "      <td>376.82</td>\n",
       "      <td>317.61</td>\n",
       "      <td>152.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3712.31</td>\n",
       "      <td>550.74</td>\n",
       "      <td>893.37</td>\n",
       "      <td>346.93</td>\n",
       "      <td>527.00</td>\n",
       "      <td>1034.98</td>\n",
       "      <td>720.33</td>\n",
       "      <td>462.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2207.58</td>\n",
       "      <td>449.37</td>\n",
       "      <td>572.40</td>\n",
       "      <td>211.92</td>\n",
       "      <td>302.09</td>\n",
       "      <td>585.23</td>\n",
       "      <td>429.77</td>\n",
       "      <td>252.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2629.16</td>\n",
       "      <td>557.32</td>\n",
       "      <td>689.73</td>\n",
       "      <td>435.69</td>\n",
       "      <td>514.66</td>\n",
       "      <td>795.87</td>\n",
       "      <td>575.76</td>\n",
       "      <td>323.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1844.78</td>\n",
       "      <td>430.29</td>\n",
       "      <td>271.28</td>\n",
       "      <td>126.33</td>\n",
       "      <td>250.56</td>\n",
       "      <td>513.18</td>\n",
       "      <td>314.00</td>\n",
       "      <td>151.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2709.46</td>\n",
       "      <td>428.11</td>\n",
       "      <td>334.12</td>\n",
       "      <td>160.77</td>\n",
       "      <td>405.14</td>\n",
       "      <td>461.67</td>\n",
       "      <td>535.13</td>\n",
       "      <td>232.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1563.78</td>\n",
       "      <td>303.65</td>\n",
       "      <td>233.81</td>\n",
       "      <td>107.90</td>\n",
       "      <td>209.70</td>\n",
       "      <td>393.99</td>\n",
       "      <td>509.39</td>\n",
       "      <td>160.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1675.75</td>\n",
       "      <td>613.32</td>\n",
       "      <td>550.71</td>\n",
       "      <td>219.79</td>\n",
       "      <td>272.59</td>\n",
       "      <td>599.43</td>\n",
       "      <td>371.62</td>\n",
       "      <td>211.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1427.65</td>\n",
       "      <td>431.79</td>\n",
       "      <td>288.55</td>\n",
       "      <td>208.14</td>\n",
       "      <td>217.00</td>\n",
       "      <td>337.76</td>\n",
       "      <td>421.31</td>\n",
       "      <td>165.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1942.23</td>\n",
       "      <td>512.27</td>\n",
       "      <td>401.39</td>\n",
       "      <td>206.06</td>\n",
       "      <td>321.29</td>\n",
       "      <td>697.22</td>\n",
       "      <td>492.60</td>\n",
       "      <td>226.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1783.43</td>\n",
       "      <td>511.88</td>\n",
       "      <td>282.84</td>\n",
       "      <td>201.01</td>\n",
       "      <td>237.60</td>\n",
       "      <td>617.74</td>\n",
       "      <td>523.52</td>\n",
       "      <td>182.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3055.17</td>\n",
       "      <td>353.23</td>\n",
       "      <td>564.56</td>\n",
       "      <td>356.27</td>\n",
       "      <td>811.88</td>\n",
       "      <td>873.06</td>\n",
       "      <td>1082.82</td>\n",
       "      <td>420.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2033.87</td>\n",
       "      <td>300.82</td>\n",
       "      <td>338.65</td>\n",
       "      <td>157.78</td>\n",
       "      <td>329.06</td>\n",
       "      <td>621.74</td>\n",
       "      <td>587.02</td>\n",
       "      <td>218.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2057.86</td>\n",
       "      <td>186.44</td>\n",
       "      <td>202.72</td>\n",
       "      <td>171.79</td>\n",
       "      <td>329.65</td>\n",
       "      <td>477.17</td>\n",
       "      <td>312.93</td>\n",
       "      <td>279.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2303.29</td>\n",
       "      <td>589.99</td>\n",
       "      <td>516.21</td>\n",
       "      <td>236.55</td>\n",
       "      <td>403.92</td>\n",
       "      <td>730.05</td>\n",
       "      <td>438.41</td>\n",
       "      <td>225.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1974.28</td>\n",
       "      <td>507.76</td>\n",
       "      <td>344.79</td>\n",
       "      <td>203.21</td>\n",
       "      <td>240.24</td>\n",
       "      <td>575.10</td>\n",
       "      <td>430.36</td>\n",
       "      <td>223.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1673.82</td>\n",
       "      <td>437.75</td>\n",
       "      <td>461.61</td>\n",
       "      <td>153.32</td>\n",
       "      <td>254.66</td>\n",
       "      <td>445.59</td>\n",
       "      <td>346.11</td>\n",
       "      <td>191.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2194.25</td>\n",
       "      <td>537.01</td>\n",
       "      <td>369.07</td>\n",
       "      <td>249.54</td>\n",
       "      <td>290.84</td>\n",
       "      <td>561.91</td>\n",
       "      <td>407.70</td>\n",
       "      <td>330.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2646.61</td>\n",
       "      <td>839.70</td>\n",
       "      <td>204.44</td>\n",
       "      <td>209.11</td>\n",
       "      <td>379.30</td>\n",
       "      <td>371.04</td>\n",
       "      <td>269.59</td>\n",
       "      <td>389.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1472.95</td>\n",
       "      <td>390.89</td>\n",
       "      <td>447.95</td>\n",
       "      <td>259.51</td>\n",
       "      <td>230.61</td>\n",
       "      <td>490.90</td>\n",
       "      <td>469.10</td>\n",
       "      <td>191.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1525.57</td>\n",
       "      <td>472.98</td>\n",
       "      <td>328.90</td>\n",
       "      <td>219.86</td>\n",
       "      <td>206.65</td>\n",
       "      <td>449.69</td>\n",
       "      <td>249.66</td>\n",
       "      <td>228.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1654.69</td>\n",
       "      <td>437.77</td>\n",
       "      <td>258.78</td>\n",
       "      <td>303.00</td>\n",
       "      <td>244.93</td>\n",
       "      <td>479.53</td>\n",
       "      <td>288.56</td>\n",
       "      <td>236.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1375.46</td>\n",
       "      <td>480.89</td>\n",
       "      <td>273.84</td>\n",
       "      <td>317.32</td>\n",
       "      <td>251.08</td>\n",
       "      <td>424.75</td>\n",
       "      <td>228.73</td>\n",
       "      <td>195.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1608.82</td>\n",
       "      <td>536.05</td>\n",
       "      <td>432.46</td>\n",
       "      <td>235.82</td>\n",
       "      <td>250.28</td>\n",
       "      <td>541.30</td>\n",
       "      <td>344.85</td>\n",
       "      <td>214.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1       2       3       4       5        6        7       8\n",
       "0   2959.19  730.79  749.41  513.34  467.87  1141.82   478.42  457.64\n",
       "1   2459.77  495.47  697.33  302.87  284.19   735.97   570.84  305.08\n",
       "2   1495.63  515.90  362.37  285.32  272.95   540.58   364.91  188.63\n",
       "3   1406.33  477.77  290.15  208.57  201.50   414.72   281.84  212.10\n",
       "4   1303.97  524.29  254.83  192.17  249.81   463.09   287.87  192.96\n",
       "5   1730.84  553.90  246.91  279.81  239.18   445.20   330.24  163.86\n",
       "6   1561.86  492.42  200.49  218.36  220.69   459.62   360.48  147.76\n",
       "7   1410.11  510.71  211.88  277.11  224.65   376.82   317.61  152.85\n",
       "8   3712.31  550.74  893.37  346.93  527.00  1034.98   720.33  462.03\n",
       "9   2207.58  449.37  572.40  211.92  302.09   585.23   429.77  252.54\n",
       "10  2629.16  557.32  689.73  435.69  514.66   795.87   575.76  323.36\n",
       "11  1844.78  430.29  271.28  126.33  250.56   513.18   314.00  151.39\n",
       "12  2709.46  428.11  334.12  160.77  405.14   461.67   535.13  232.29\n",
       "13  1563.78  303.65  233.81  107.90  209.70   393.99   509.39  160.12\n",
       "14  1675.75  613.32  550.71  219.79  272.59   599.43   371.62  211.84\n",
       "15  1427.65  431.79  288.55  208.14  217.00   337.76   421.31  165.32\n",
       "16  1942.23  512.27  401.39  206.06  321.29   697.22   492.60  226.45\n",
       "17  1783.43  511.88  282.84  201.01  237.60   617.74   523.52  182.52\n",
       "18  3055.17  353.23  564.56  356.27  811.88   873.06  1082.82  420.81\n",
       "19  2033.87  300.82  338.65  157.78  329.06   621.74   587.02  218.27\n",
       "20  2057.86  186.44  202.72  171.79  329.65   477.17   312.93  279.19\n",
       "21  2303.29  589.99  516.21  236.55  403.92   730.05   438.41  225.80\n",
       "22  1974.28  507.76  344.79  203.21  240.24   575.10   430.36  223.46\n",
       "23  1673.82  437.75  461.61  153.32  254.66   445.59   346.11  191.48\n",
       "24  2194.25  537.01  369.07  249.54  290.84   561.91   407.70  330.95\n",
       "25  2646.61  839.70  204.44  209.11  379.30   371.04   269.59  389.33\n",
       "26  1472.95  390.89  447.95  259.51  230.61   490.90   469.10  191.34\n",
       "27  1525.57  472.98  328.90  219.86  206.65   449.69   249.66  228.19\n",
       "28  1654.69  437.77  258.78  303.00  244.93   479.53   288.56  236.51\n",
       "29  1375.46  480.89  273.84  317.32  251.08   424.75   228.73  195.93\n",
       "30  1608.82  536.05  432.46  235.82  250.28   541.30   344.85  214.40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=city.iloc[:,1:]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f43a06c-2c12-4606-91dd-71c77c4e3f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3938ae09-0914-4106-93cc-bf93135682c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"OMP_NUM_THREADS\"] = '1'\n",
    "from sklearn.cluster import KMeans\n",
    "km=KMeans(n_clusters=2)\n",
    "label = km.fit_predict(data)\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fc2295c-535f-4e80-9181-ecaf2f5240bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2809.37      ,  568.16875   ,  581.14625   ,  320.19125   ,\n",
       "         474.245     ,  768.0575    ,  583.9125    ,  352.0425    ],\n",
       "       [1692.41347826,  461.56173913,  331.58173913,  217.98434783,\n",
       "         254.24391304,  500.53304348,  376.96434783,  205.13304348]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28968127-11b6-4525-99bd-38e8c28e55dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6457.13375   , 4040.41565217])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expenses = np.sum(km.cluster_centers_,axis=1)\n",
    "expenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11d0cf28-a549-4bea-8827-f615c02ca398",
   "metadata": {},
   "outputs": [],
   "source": [
    "CityCluster = [[],[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9f3aecc-01bd-465b-a8c9-e886da012221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['北京', '天津', '上海', '浙江', '福建', '广东', '重庆', '西藏'],\n",
       " ['河北',\n",
       "  '山西',\n",
       "  '内蒙古',\n",
       "  '辽宁',\n",
       "  '吉林',\n",
       "  '黑龙江',\n",
       "  '江苏',\n",
       "  '安徽',\n",
       "  '江西',\n",
       "  '山东',\n",
       "  '河南',\n",
       "  '湖南',\n",
       "  '湖北',\n",
       "  '广西',\n",
       "  '海南',\n",
       "  '四川',\n",
       "  '贵州',\n",
       "  '云南',\n",
       "  '陕西',\n",
       "  '甘肃',\n",
       "  '青海',\n",
       "  '宁夏',\n",
       "  '新疆']]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(cityName)):\n",
    "    CityCluster[label[i]].append(cityName[i])\n",
    "CityCluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "222ca6fa-76a6-4328-a93a-abac0e7b35ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expenses:6457.13\n",
      "['北京', '天津', '上海', '浙江', '福建', '广东', '重庆', '西藏']\n",
      "Expenses:4040.42\n",
      "['河北', '山西', '内蒙古', '辽宁', '吉林', '黑龙江', '江苏', '安徽', '江西', '山东', '河南', '湖南', '湖北', '广西', '海南', '四川', '贵州', '云南', '陕西', '甘肃', '青海', '宁夏', '新疆']\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(CityCluster)):\n",
    "    print('Expenses:%.2f' % expenses[i])\n",
    "    print(CityCluster[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a6948a7-48de-4d11-9e34-430090437b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 ============= 刘显婷 ============== 224180117\n"
     ]
    }
   ],
   "source": [
    "print(\"信计221 ============= 刘显婷 ============== 224180117\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "170a3630-233f-47b3-8800-554e26d822c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expenses: 6601.84\n",
      "['北京', '天津', '上海', '浙江', '福建', '广东', '西藏']\n",
      "Expenses: 3497.85\n",
      "['山西', '内蒙古', '黑龙江', '河南', '宁夏']\n",
      "Expenses: 4757.15\n",
      "['江苏', '湖南', '广西', '海南', '重庆', '四川', '云南']\n",
      "Expenses: 3965.37\n",
      "['河北', '辽宁', '吉林', '安徽', '江西', '山东', '湖北', '贵州', '陕西', '甘肃', '青海', '新疆']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import warnings\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n",
    "warnings.filterwarnings(\"ignore\", message=\"KMeans is known to have a memory leak on Windows\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "# 读取城市数据文件，这里假设文件路径是D:/Users/19202/Desktop/city.txt\n",
    "# 数据没有表头，使用GBK编码。\n",
    "city = pd.read_csv(r\"D:/Users/19202/Desktop/city.txt\", header=None, encoding='gbk')\n",
    "# 提取城市名称作为数组。\n",
    "cityName = city.iloc[:, 0].values\n",
    "# 提取除城市名称外的数据作为特征数据。\n",
    "data = city.iloc[:, 1:]\n",
    "# 初始化KMeans聚类模型，设置聚类数为4。\n",
    "km = KMeans(n_clusters=4)\n",
    "# 使用数据进行拟合，并预测每个城市的聚类标签。\n",
    "label = km.fit_predict(data)\n",
    "# 计算每个聚类中心的总和，这里可能是指聚类中心的总费用。\n",
    "expenses = np.sum(km.cluster_centers_, axis=1)\n",
    "# 初始化一个列表，用于存储每个聚类的城市名称。\n",
    "CityCluster = [[] for _ in range(4)]\n",
    "# 遍历每个城市，根据聚类标签将城市名称添加到对应的聚类列表中。\n",
    "for i in range(len(cityName)):\n",
    "    CityCluster[label[i]].append(cityName[i])\n",
    "# 遍历每个聚类，打印聚类中心的总费用和该聚类中的城市名称。\n",
    "for i in range(len(CityCluster)):\n",
    "    print('Expenses: %.2f' % expenses[i])\n",
    "    print(CityCluster[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0c420d0-0103-4f2e-8052-53ed218bef66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 ============= 刘显婷 ============== 224180117\n"
     ]
    }
   ],
   "source": [
    "print(\"信计221 ============= 刘显婷 ============== 224180117\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1817257e-fd7f-4593-b10d-acbdb744dd53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 ============= 刘显婷 ============== 224180117\n",
      "Labels: [ 0 -1  0  1 -1  1  0  1  2 -1  1  0  1  1  3 -1 -1  3 -1  1  1 -1  1  3\n",
      "  4 -1  1  1  2  0  2  2 -1  0  1  0  0  0  1  3 -1  0  1  1  0  0  2 -1\n",
      "  1  3  1 -1  3 -1  3  0  1  1  2  3  3 -1 -1 -1  0  1  2  1 -1  3  1  1\n",
      "  2  3  0  1 -1  2  0  0  3  2  0  1 -1  1  3 -1  4  2 -1 -1  0 -1  3 -1\n",
      "  0  2  1 -1 -1  2  1  1  2  0  2  1  1  3  3  0  1  2  0  1  0 -1  1  1\n",
      "  3 -1  2  1  3  1  1  1  2 -1  5 -1  1  3 -1  0  1  0  0  1 -1 -1 -1  2\n",
      "  2  0  1  1  3  0  0  0  1  4  4 -1 -1 -1 -1  4 -1  4  4 -1  4 -1  1  2\n",
      "  2  3  0  1  0 -1  1  0  1 -1 -1  0  2  1  0  2 -1  1  1 -1 -1  0  1  1\n",
      " -1  3  1  1 -1  1  1  0  0 -1  0 -1  0  0  2 -1  1 -1  1  0 -1  2  1  3\n",
      "  1  1 -1  1  0  0 -1  0  0  3  2  0  0  5 -1  3  2 -1  5  4  4  4 -1  5\n",
      "  5 -1  4  0  4  4  4  5  4  4  5  5  0  5  4 -1  4  5  5  5  1  5  5  0\n",
      "  5  4  4 -1  4  4  5  4  0  5  4 -1  0  5  5  5 -1  4  5  5  5  5  4  4]\n",
      "Estimated number of clusters: 6\n",
      "Silhouette Coefficient: 0.709\n",
      "Cluster 0 :\n",
      "[22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0]\n",
      "Cluster 1 :\n",
      "[23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0]\n",
      "Cluster 2 :\n",
      "[20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0]\n",
      "Cluster 3 :\n",
      "[21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0]\n",
      "Cluster 4 :\n",
      "[8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]\n",
      "Cluster 5 :\n",
      "[7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印出班级、姓名和学号\n",
    "print(\"信计221 ============= 刘显婷 ============== 224180117\")\n",
    "# 导入所需的库\n",
    "import numpy as np  # 用于数学运算\n",
    "import pandas as pd  # 用于数据处理\n",
    "import sklearn.cluster as skc  # 用于聚类分析\n",
    "from sklearn import metrics  # 用于评估聚类效果\n",
    "import matplotlib.pyplot as plt  # 用于绘图\n",
    "# 读取数据\n",
    "data = pd.read_csv(r\"D:/Users/19202/Desktop/student.txt\", header=None)\n",
    "# 提取开始上网时间（小时），并将其转换为小时数\n",
    "starttime = pd.to_datetime(data.iloc[:,4], errors='coerce').dt.hour\n",
    "x = starttime[~starttime.isnull()].values.reshape(-1,1)  # 去除空值并重塑为二维数组\n",
    "# 使用DBSCAN算法进行聚类\n",
    "db = skc.DBSCAN(eps=0.01, min_samples=20).fit(x)\n",
    "# 获取聚类标签\n",
    "labels = db.labels_\n",
    "# 打印聚类标签\n",
    "print('Labels:', labels)\n",
    "# 计算噪声数据（标签为-1）的比例\n",
    "raito = len(labels[labels[:] == -1]) / len(labels)\n",
    "# 计算簇的个数并打印，评价聚类效果\n",
    "# 如果labels中存在-1，则减去1，因为-1表示噪声点\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "# 计算并打印轮廓系数，评价聚类效果\n",
    "print('Silhouette Coefficient: %.3f' % metrics.silhouette_score(x, labels))\n",
    "# 打印各簇标号以及内数据\n",
    "for i in range(n_clusters_):\n",
    "    print('Cluster', i, ':')\n",
    "    print(list(x[labels == i].flatten()))  # 打印每个簇中的小时数\n",
    "# 画直方图，24个bin表示24小时\n",
    "plt.hist(x, 24, edgecolor='white')\n",
    "plt.show()  # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2ae51c3-15cd-48f9-bf76-869147202d8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 ============= 刘显婷 ============== 224180117\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>球员</th>\n",
       "      <th>球队</th>\n",
       "      <th>得分</th>\n",
       "      <th>命中-出手</th>\n",
       "      <th>命中率</th>\n",
       "      <th>命中-三分</th>\n",
       "      <th>三分命中率</th>\n",
       "      <th>命中-罚球</th>\n",
       "      <th>罚球命中率</th>\n",
       "      <th>场次</th>\n",
       "      <th>上场时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>扬尼斯-阿德托昆博</td>\n",
       "      <td>雄鹿</td>\n",
       "      <td>32.9</td>\n",
       "      <td>13.20-21.60</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.20-0.80</td>\n",
       "      <td>0.214</td>\n",
       "      <td>6.40-10.50</td>\n",
       "      <td>0.612</td>\n",
       "      <td>17</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>拉梅洛-鲍尔</td>\n",
       "      <td>黄蜂</td>\n",
       "      <td>31.1</td>\n",
       "      <td>10.70-24.90</td>\n",
       "      <td>0.430</td>\n",
       "      <td>4.70-13.10</td>\n",
       "      <td>0.356</td>\n",
       "      <td>4.90-5.80</td>\n",
       "      <td>0.848</td>\n",
       "      <td>18</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>谢伊-吉尔杰斯-亚历山大</td>\n",
       "      <td>雷霆</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.40-20.60</td>\n",
       "      <td>0.504</td>\n",
       "      <td>2.10-6.10</td>\n",
       "      <td>0.344</td>\n",
       "      <td>7.10-8.20</td>\n",
       "      <td>0.855</td>\n",
       "      <td>20</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>尼古拉-约基奇</td>\n",
       "      <td>掘金</td>\n",
       "      <td>29.6</td>\n",
       "      <td>11.10-19.80</td>\n",
       "      <td>0.562</td>\n",
       "      <td>2.20-4.30</td>\n",
       "      <td>0.508</td>\n",
       "      <td>5.10-6.30</td>\n",
       "      <td>0.819</td>\n",
       "      <td>15</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>杰森-塔特姆</td>\n",
       "      <td>凯尔特人</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.20-20.10</td>\n",
       "      <td>0.460</td>\n",
       "      <td>4.00-10.50</td>\n",
       "      <td>0.381</td>\n",
       "      <td>6.40-8.10</td>\n",
       "      <td>0.801</td>\n",
       "      <td>20</td>\n",
       "      <td>36.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  排名            球员    球队    得分        命中-出手    命中率       命中-三分  三分命中率  \\\n",
       "1  1     扬尼斯-阿德托昆博    雄鹿  32.9  13.20-21.60  0.609   0.20-0.80  0.214   \n",
       "2  2        拉梅洛-鲍尔    黄蜂  31.1  10.70-24.90  0.430  4.70-13.10  0.356   \n",
       "3  3  谢伊-吉尔杰斯-亚历山大    雷霆  30.0  10.40-20.60  0.504   2.10-6.10  0.344   \n",
       "4  4       尼古拉-约基奇    掘金  29.6  11.10-19.80  0.562   2.20-4.30  0.508   \n",
       "5  5        杰森-塔特姆  凯尔特人  29.0   9.20-20.10  0.460  4.00-10.50  0.381   \n",
       "\n",
       "        命中-罚球  罚球命中率  场次  上场时间  \n",
       "1  6.40-10.50  0.612  17  35.2  \n",
       "2   4.90-5.80  0.848  18  34.1  \n",
       "3   7.10-8.20  0.855  20  34.6  \n",
       "4   5.10-6.30  0.819  15  37.5  \n",
       "5   6.40-8.10  0.801  20  36.4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"信计221 ============= 刘显婷 ============== 224180117\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "# 初始化一个空列表，用于存储从网页抓取的数据\n",
    "table = []\n",
    "# 循环遍历1到6，对应不同的网页页面\n",
    "for i in range(1,7):\n",
    "    # 使用pandas的read_html函数从网页抓取表格数据\n",
    "    table.append(pd.read_html('https://nba.hupu.com/stats/players/pts/%d' % i)[0].drop(0))\n",
    "# 将列表中的所有DataFrame合并成一个DataFrame\n",
    "players = pd.concat(table)\n",
    "# 去除合并后的DataFrame中的空值\n",
    "players = players.dropna()\n",
    "# 定义列名\n",
    "columns = ['排名','球员','球队','得分','命中-出手','命中率','命中-三分','三分命中率','命中-罚球','罚球命中率','场次','上场时间']\n",
    "# 将列名赋给DataFrame\n",
    "players.columns = columns\n",
    "# 将得分列的数据类型转换为浮点数\n",
    "players['得分'] = players['得分'].astype('float')\n",
    "# 将命中率列的数据类型转换为浮点数，并去除百分号\n",
    "# 例如，将 \"50%\" 转换为 0.50\n",
    "players['命中率'] = players['命中率'].str[:-1].astype('float') / 100\n",
    "# 将三分命中率列的数据类型转换为浮点数，并去除百分号\n",
    "players['三分命中率'] = players['三分命中率'].str[:-1].astype('float') / 100\n",
    "# 将罚球命中率列的数据类型转换为浮点数，并去除百分号\n",
    "players['罚球命中率'] = players['罚球命中率'].str[:-1].astype('float') / 100\n",
    "# 将场次列的数据类型转换为整数\n",
    "players['场次'] = players['场次'].astype('int')\n",
    "# 将上场时间列的数据类型转换为浮点数\n",
    "players['上场时间'] = players['上场时间'].astype('float')\n",
    "# 打印DataFrame的前五行，以便查看数据\n",
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47fc3c49-590e-4bba-a0a6-07aee9f0fdc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 中文和负号的正常显示\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 设置绘图风格\n",
    "plt.style.use('ggplot')\n",
    "# 绘制得分与命中率的散点图\n",
    "players.plot(x='罚球命中率',y='命中率',kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2bab3a9-be04-4631-8bf7-4f51fe256323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 假设players是一个DataFrame\n",
    "X = players[['罚球命中率','命中率']]  # 选取特征\n",
    "K = range(1, int(np.sqrt(players.shape[0])) + 1)  # 聚类个数从1到sqrt(n)\n",
    "GSSE = []  # 用于存储每个K值的SSE\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=10)  # 创建KMeans模型\n",
    "    kmeans.fit(X)  # 拟合数据\n",
    "    labels = kmeans.labels_  # 聚类标签\n",
    "    centers = kmeans.cluster_centers_  # 聚类中心    \n",
    "    # 计算SSE（每个簇内点与簇中心的距离平方和）\n",
    "    SSE = 0\n",
    "    for i in range(k):\n",
    "        # 获取该簇的所有数据点\n",
    "        cluster_points = X[labels == i]\n",
    "        \n",
    "        # 计算簇内的距离平方和\n",
    "        distance = np.sum((cluster_points.values - centers[i])**2)  # 使用 .values 转换为numpy数组\n",
    "        SSE += distance   \n",
    "    GSSE.append(SSE)  # 存储当前K值的SSE\n",
    "# 绘制K与GSSE的关系\n",
    "plt.plot(K, GSSE, 'b*-')\n",
    "plt.xlabel('聚类个数 K')\n",
    "plt.ylabel('簇内离差平方和 SSE')\n",
    "plt.title('选择最优的聚类个数 K')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4126d689-ff55-48aa-b974-fb4f5fc0407b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#调用sklearn的库函数\n",
    "num_clusters = 6\n",
    "kmeans = KMeans(n_clusters=num_clusters, random_state=1)\n",
    "kmeans.fit(X)\n",
    "# 聚类结果标签\n",
    "players['cluster'] = kmeans.labels_\n",
    "# 聚类中心\n",
    "centers = kmeans.cluster_centers_\n",
    "# 绘制散点图\n",
    "plt.scatter(x = X.iloc[:,0], y = X.iloc[:,1], c = players['cluster'], s=50, cmap='rainbow')\n",
    "plt.scatter(centers[:,0], centers[:,1], c='k', marker = '*', s = 180)\n",
    "plt.xlabel('罚球命中率')\n",
    "plt.ylabel('命中率')\n",
    "# 图形显示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb4b4fdb-c745-4f54-8bb9-02478c2ee13e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
